| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Chicago Steel | USHL | 4 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2024-25 | Chicago Steel | USHL | 49 | 2 | 6 | 8 | 0.163 | 0.1004 | 0.1010 | 0.4811 | 0.4841 |
| 2025-26 | Chicago Steel | USHL | 33 | 2 | 11 | 13 | 0.394 | 0.2421 | 0.2319 | 1.1605 | 1.1116 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Western Michigan | D1 | NCHC | — | 17 | 1 | 3 | 4 | 0.235 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.